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library(dplyr)
library(readr)
library(ggplot2)
library(tigris)
library(sf)
library(DT)How Many Fast Food Restaurants per Capita Are in Each State/County?
Introduction
Data Acquisition
NaNDA Eating and Drinking Places by Census Tract & ZCTA, 1990–2021
To acquire the national fast food count per state and county, this project will use the NaNDA Eating and Drinking Places by Census Tract & ZCTA, 1990-2021 dataset and filter year 2019 for more accurate pre-COVID numbers. The dataset at census tract level is used.
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# Cache shapefiles locally (faster repeat runs)
options(tigris_use_cache = TRUE)
# load dataset
eatdrink_tract <- read_csv("nanda_eatdrink_Tract20_1990-2021_01P.csv")
fastfood_raw <- eatdrink_tract |>
filter(year == 2019) |>
arrange(desc(count_fastfood)) |>
select(tract_fips20,totpop,count_fastfood,den_fastfood,)
tibble(fastfood_raw)# A tibble: 85,528 × 4
tract_fips20 totpop count_fastfood den_fastfood
<chr> <dbl> <dbl> <dbl>
1 06073008511 4989 22 4.41
2 12086009010 6716 22 3.28
3 06059062610 10440 20 1.92
4 12023110500 7764 20 2.58
5 17031839100 7545 18 2.39
6 04013216816 6656 17 2.55
7 12095017001 4652 16 3.44
8 06071002109 6121 15 2.45
9 29183312400 5089 15 2.95
10 40109108604 3664 15 4.09
# ℹ 85,518 more rowsU.S. Census Bureau ACS B01003 (2019)
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pop_2019 <- read_csv("ACSDT5Y2019.B01003-Data.csv")
pop_clean <- pop_2019 |>
mutate(tract_fips20 = substr(GEO_ID, nchar(GEO_ID) - 10, nchar(GEO_ID)))
fastfood_tract <- fastfood_raw |>
left_join(pop_clean, by = "tract_fips20") |>
select(tract_fips20,GEO_ID,totpop,B01003_001E,den_fastfood,count_fastfood) |>
rename(acs_pop = B01003_001E) |>
mutate(acs_pop = as.numeric(acs_pop)) |>
filter(acs_pop > 0)
datatable(fastfood_tract)Fast Food Restaurants per 1000 people by COUNTY
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#State look up
state_lookup <- states(year = 2019) |>
st_drop_geometry() |>
select(STATEFP, STUSPS, NAME) |>
rename(
State_Abbr = STUSPS,
State_Name = NAME
)
# Aggregate tract to county
den_fastfood_county <- fastfood_tract |>
mutate(county_fips20 = substr(tract_fips20, 1, 5)) |>
group_by(county_fips20) |>
summarize(
total_fastfood = sum(count_fastfood, na.rm = TRUE),
total_pop = sum(acs_pop, na.rm = TRUE),
den_fastfood = (total_fastfood / total_pop) * 1000,
.groups = "drop"
) |>
filter(total_pop > 0)
# Load shapefile and join
county_shapes <- counties(year = 2019, cb = TRUE) |>
mutate(GEOID = as.character(GEOID))
fastfood_map_county <- county_shapes |>
left_join(den_fastfood_county, by = c("GEOID" = "county_fips20")) |>
left_join(state_lookup, by = "STATEFP") |>
mutate(
den_fastfood_plot = ifelse(den_fastfood == 0, NA, den_fastfood)
)
# Create datatable
datatable(
fastfood_map_county |>
st_drop_geometry() |>
arrange(desc(den_fastfood)) |>
select(
County = NAME,
State = State_Abbr,
State_Name,
total_fastfood,
total_pop,
den_fastfood
),
caption = "Fast Food Restaurants per 1,000 People (County, 2019)",
options = list(pageLength = 10, scrollX = TRUE)
) |>
formatRound("total_fastfood", 0, mark = ",") |>
formatRound("den_fastfood", 2)Show code
fastfood_map_county |>
st_drop_geometry() |>
ggplot(aes(x = den_fastfood)) +
geom_histogram(
bins = 30,
fill = "#9badff",
color = "white",
linewidth = 0.3
) +
coord_cartesian(xlim = c(0, 1)) +
labs(
title = "Fast Food Restaurants per 1,000 People",
subtitle = "Distribution of Fast Food Density per County, 2019",
caption = "Source: NaNDA Eating and Drinking Places by Census Tract & ZCTA, 1990–2021\nU.S. Census Bureau ACS B01003 (2019)",
x = "Fast Food Density",
y = "# of Counties"
) +
theme(
axis.line = element_line(color = "black", linewidth = 0.4),
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.margin = margin(5, 5, 5, 5),
plot.caption = element_text(size = 10, face = "italic", hjust = 0),
panel.background = element_rect(fill = "white")
)
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ggplot(fastfood_map_county) +
geom_sf(aes(fill = den_fastfood), color = NA, linewidth = 0.1) +
scale_fill_gradientn(
colours = c("grey90", "#e792e7", "#5b7cff", "#0432FF"),
name = "Fast Food\nDensity",
limits = c(NA, NA),
na.value = "#ffffff",
trans = "sqrt"
) +
coord_sf(
xlim = c(-125, -66),
ylim = c(24, 50),
expand = FALSE
) +
labs(
title = "Fast Food Restaurants per 1,000 People",
subtitle = "County, 2019",
caption = "Source: NaNDA Eating and Drinking Places by Census Tract & ZCTA, 1990–2021\nU.S. Census Bureau ACS B01003 (2019)"
) +
theme_void() +
theme(
legend.position = "right",
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.margin = margin(5, 5, 5, 5),
plot.caption = element_text(size = 10, face = "italic", hjust = 0)
)
Fast Food Density State
Datatable
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# Aggregate tract to state
fastfood_state <- fastfood_tract |>
mutate(state_fips20 = substr(tract_fips20, 1, 2)) |>
group_by(state_fips20) |>
summarize(
total_fastfood = sum(count_fastfood, na.rm = TRUE),
total_pop = sum(acs_pop, na.rm = TRUE),
den_fastfood = (total_fastfood / total_pop) * 1000,
.groups = "drop"
) |>
filter(total_pop > 0)
# Load state shapefile and join
state_shapes <- states(year = 2019, cb = TRUE) |>
mutate(STATEFP = as.character(STATEFP))
fastfood_map_state <- state_shapes |>
left_join(fastfood_state, by = c("STATEFP" = "state_fips20"))
# Creat datatable
datatable(
fastfood_map_state |>
st_drop_geometry() |>
arrange(desc(den_fastfood)) |>
slice_max(den_fastfood, n = 10) |>
select(State = NAME, den_fastfood,total_pop,total_fastfood),
rownames = FALSE,
caption = "Fast Food Restaurants per 1,000 People (State, 2019)",
options = list(
searching = FALSE,
scrollX = FALSE,
autoWidth = TRUE
)
) |>
formatRound("den_fastfood", 2)Heat Map
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# Create heat map
ggplot(fastfood_map_state) +
geom_sf(aes(fill = den_fastfood), color = "white") +
scale_fill_gradient(
low = "#ffffff",
high = "#932092",
name = "Fast Food\nDensity", limits = c(NA,NA)
)+
coord_sf(
xlim = c(-125, -66),
ylim = c(24, 50),
expand = FALSE
) +
labs(
title = "Fast Food Restaurants per 1,000 People",
subtitle = "State, 2019",
caption = "Source: NaNDA Eating and Drinking Places by Census Tract & ZCTA, 1990–2021\nU.S. Census Bureau ACS B01003 (2019)"
) +
theme_void() +
theme(
legend.position = "right",
plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
plot.margin = margin(5, 5, 5, 5),
plot.caption = element_text(size = 10, face = "italic", hjust = 0)
)
Top 10 States Highest Fast Food Concentration
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# Rank and flag top 10 states
fastfood_map_state_topflag <- fastfood_map_state |>
arrange(desc(den_fastfood)) |>
mutate(
rank = row_number(),
den_top10 = ifelse(rank <= 10, den_fastfood, NA_real_)
)
ggplot(fastfood_map_state_topflag) +
geom_sf(aes(fill = den_top10),
color = "white",
linewidth = 0.3) +
scale_fill_distiller(
palette = "Blues", # Nice clean blues
direction = 1, # Light → dark
na.value = "#e0e0e0", # Light grey for non–top 10
name = "Fast-Food per\n1,000 People"
) +
labs(
title = "Top 10 States with the Most Fast-Food Restaurants per 1,000 People",
subtitle = "Non–Top 10 States Shown in Grey",
caption = "Source: NaNDA Tract20 (1990–2021) & U.S. Census ACS 2019"
) +
theme_void() +
theme(
legend.position = "right",
plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 12),
plot.caption = element_text(size = 9, hjust = 0),
plot.margin = margin(10, 10, 10, 10)
)